Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes.

In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC50, prolonged_14D_NOEC, prolonged_21D_LC50, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R2 =0.738-0.869, QLOO2 =0.712-0.831, Q(F1)2 =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.

Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation.

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health.

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762-0.844; Q2F2: 0.759-0.831 and MAEtest: 0.195-0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions.

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183.

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across.

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

First report on ecotoxicological QSTR and i-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species.

The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies QSTR models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that non-polar characteristics, electronegativity, hydrogen bonding, π-π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.

Rottlerin renders a selective and highly potent CYP2C8 inhibition to impede EET formation for implication in cancer therapy.

CYP2C8 is a crucial CYP isoform responsible for the metabolism of xenobiotics and endogenous molecules. CYP2C8 converts arachidonic acid to epoxyeicosatrienoic acids (EETs) that cause cancer progression. Rottlerin possess significant anticancer actions. However, information on its CYP inhibitory action is lacking in the literature and therefore, we aimed to explore the same using in silico, in vitro, and in vivo approaches. Rottlerin showed highly potent and selective CYP2C8 inhibition (IC50 < 0.1 µM) compared to negligible inhibition (IC50 > 10 µM) for seven other experimental CYPs in human liver microsomes (HLM) (in vitro) using USFDA recommended index reactions. Mechanistic studies reveal that rottlerin could reversibly (mixed-type) block CYP2C8. Molecular docking (in silico) results indicate a strong interaction could occur between rottlerin and the active site of human CYP2C8. Rottlerin boosted the plasma exposure of repaglinide and paclitaxel (CYP2C8 substrates) by delaying their metabolism using the rat model (in vivo). Multiple-dose treatment of rottlerin with CYP2C8 substrates lowered the CYP2C8 protein expression and up-regulated & down-regulated the mRNA for CYP2C12 & CYP2C11 (rat homologs), respectively, in rat liver tissue. Rottlerin substantially hindered the EET formation in HLM. Overall results of rottlerin on CYP2C8 inhibition and EET formation insinuate further exploration for cancer therapy.

Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across.

Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.

Assessment of superiority of HSP70-targeting aptamer-functionalized drug-nanocarrier over non-targeted commercially available counterpart in HCC therapy: in vitro and in vivo investigations and molecular modeling.

AIMS: This research aims to compare the therapeutic potential of target-specific phosphorothioate backbone-modified aptamer L5 (TLS9a)-functionalized paclitaxel (PTX)-loaded nanocarrier (PTX-NPL5) that we formulated with that of non-targeted commercial formulation, protein albumin-bound nanoparticles of PTX, Abraxane® (CF) against hepatocellular carcinoma (HCC) through a myriad of preclinical investigations. MAIN METHODS: A variety of in vitro and in vivo assays have been executed to compare the therapeutic effects of the formulations under investigation, including the investigation of the degree of apoptosis induction and its mechanism, cell cycle analysis, the level of ROS production, and redox status, the morphological and histological characteristics of malignant livers, and in vivo imaging. The formulations were also compared concerning pharmacokinetic behaviors. Finally, in silico molecular docking has been performed to predict the possible interactions between aptamer and target(s). KEY FINDINGS: PTX-NPL5 exhibited therapeutic superiority over CF in terms of inducing apoptosis, cell cycle arrest, endorsing oxidative stress to neoplastic cells, and reducing hepatic cancerous lesions. Unlike CF, PTX-NPL5 did not exhibit any significant toxicity in healthy hepatocytes, proving enough impetus regarding the distinctive superiority of PTX-NPL5 over CF. The pharmacokinetic analysis further supported superior penetration and retention of PTX-NPL5 in neoplastic hepatocytes compared to CF. A molecular modeling study proposed possible interaction between aptamer L5 and heat shock protein 70 (HSP70). SIGNIFICANCE: The target-specificity of PTX-NPL5 towards neoplastic hepatocytes, probably achieved through HSP70 recognition, enhanced its therapeutic efficacy over CF, which may facilitate its real clinical deployment against HCC in the near future.

Assessment of the CYP1A2 Inhibition-Mediated Drug Interaction Potential for Pinocembrin Using In Silico, In Vitro, and In Vivo Approaches.

Pinocembrin, a bioflavonoid, is extensively used in complementary/alternative medicine. It turns out as a promising candidate against neurodegenerative diseases because of its multifaceted pharmacological action toward neuroprotection. However, literature evidence is still lacking for its inhibitory action on CYP1A2, which is responsible for xenobiotic metabolism leading to the generation of toxic metabolites and bioactivation of procarcinogens. In the present study, our aim was to evaluate the CYP1A2 inhibitory potential of pinocembrin via in silico, in vitro, and in vivo investigations. From the results of in vitro studies, pinocembrin is found to be a potent and competitive inhibitor of CYP1A2. In vitro-in vivo extrapolation results indicate the potential of pinocembrin to interact with CYP1A2 substrate drugs clinically. Molecular docking-based in silico studies demonstrate the strong interaction of pinocembrin with human CYP1A2. In in vivo investigations using a rat model, pinocembrin displayed a marked alteration in the plasma exposure of CYP1A2 substrate drugs, namely, caffeine and tacrine. In conclusion, pinocembrin has a potent CYP1A2 inhibitory action to cause drug interactions, and further confirmatory study is warranted at the clinical level.

Effect of Myricetin on CYP2C8 Inhibition to Assess the Likelihood of Drug Interaction Using In Silico, In Vitro, and In Vivo Approaches.

Myricetin, a bioflavonoid, is widely used as functional food/complementary medicine and has promising multifaceted pharmacological actions against therapeutically validated anticancer targets. On the other hand, CYP2C8 is not only crucial for alteration in the pharmacokinetics of drugs to cause drug interaction but also unequivocally important for the metabolism of endogenous substances like the formation of epoxyeicosatrienoic acids (EETs), which are considered as signaling molecules against hallmarks of cancer. However, there is hardly any information known to date about the effect of myricetin on CYP2C8 inhibition and, subsequently, the CYP2C8-mediated drug interaction potential of myricetin at the preclinical/clinical level. We aimed here to explore the CYP2C8 inhibitory potential of myricetin using in silico, in vitro, and in vivo investigations. In the in vitro study, myricetin showed a substantial effect on CYP2C8 inhibition in human liver microsomes using CYP2C8-catalyzed amodiaquine-N-deethylation as an index reaction. Considering the Lineweaver-Burk plot, the Dixon plot, and the higher α-value, myricetin is found to be a mixed type of CYP2C8 inhibitor. Moreover, in vitro-in vivo extrapolation data suggest that myricetin is likely to cause drug interaction at the hepatic level. The molecular docking study depicted a strong interaction between myricetin and the active site of the human CYP2C8 enzyme. Moreover, myricetin caused considerable elevation in the oral exposure of amodiaquine as a CYP2C8 substrate via a slowdown of amodiaquine clearance in the rat model. Overall, the potent action of myricetin on CYP2C8 inhibition indicates that there is a need for further exploration to avoid drug interaction-mediated precipitation of obvious adverse effects as well as to optimize anticancer therapy.

Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata.

The current quantitative structure-activity relationship (QSAR) study seeks to explore the underlying causes of fluctuations in growth rate and biomass of microalgae mainly due to textile dyes. The derived QSAR models cover two endpoints: ErC50 (growth rate) and EbC50 (biomass) of Raphidocelis subcapitata. In order to extract the structural features involved, multiple PLS (partial least squares) models have been developed with easy to interpret and uncomplicated 2D descriptors having proper physico-chemical meaning. These descriptors were calculated from Dragon, SiRMS, and PaDEL-descriptor software. Then, the models were developed initially using stepwise regression followed by partial least squares (PLS) regression, and the model development procedure for both the endpoints (ErC50 and EbC50) followed the stringent Organization for Economic Cooperation and Development (OECD) rules. Later on, the model validation was carried out with statistically significant and internationally accepted metrics (both internally and externally) in both the cases. Next, we have used the "Intelligent Consensus Predictor" tool (available from http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to test the prediction quality with an "intelligent" approach to select multiple models. The estimated prediction quality for the appropriate test sets reveals that the consensus models (CM) surpass the quality shown by individual models (IM) for both the endpoints (ErC50 and EbC50). Finally, the developed models were able to identify the major contributing features (hydrophobic units, unsaturation, saturation, electronegativity, branched atoms and charged fragments) related to aquatic toxicity of textile dyes.

Amalgamation of in-silico, in-vitro and in-vivo approach to establish glabridin as a potential CYP2E1 inhibitor.

CYP2E1 is directly or indirectly involved in the metabolism of ethanol and endogenous fatty acids but it plays a major role in the bio-activation of toxic substances that produce reactive metabolites leading to hepatotoxicity. Therefore, identification of CYP2E1 inhibitor from bioflavonoids class having useful pharmacological properties has dual benefit regarding avoidance of severe food-drug/nutraceutical-drug interaction and scope to develop a phytotherapeutics through an intended pharmacokinetic interaction.In the present study, we aimed to identify CYP2E1 inhibitor from experimental bioflavonoids which are unexplored for CYP2E1 inhibition till date using in-silico, in-vitro and in-vivo approaches.Results of in-vitro CYP2E1 inhibitory studies using CYP2E1-mediated chlorzoxazone 6-hydroxylation in human liver microsomes showed that glabridin have the highest potential than fisetin, epicatechin, nobiletin, and chrysin to inhibit CYP2E1 enzyme. Mechanistic investigations indicate that glabridin is a competitive CYP2E1 inhibitor. Molecular docking study results demonstrate that glabridin strongly interacted with the active site of human CYP2E1 enzyme. Pharmacokinetics of a CYP2E1 substrate in mice model indicates a significant alteration of chlorzoxazone and 6-hydroxychlorzoxazone plasma levels in the presence of glabridin. Further studies are needed to confirm the results at clinical level.Overall, glabridin is found to be a potential CYP2E1 inhibitor.

Recent advances in quantitative structure-activity relationship models of antimalarial drugs.

INTRODUCTION: Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates. AREAS COVERED: The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019. EXPERT OPINION: Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to.

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors.

BACKGROUND: Alzheimer's disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer's disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. METHODS: In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. RESULTS: The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. CONCLUSION: Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs).

Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q2 = 0.648, R2pred or Q2F1 = 0.734 and Q2F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.

QSAR modeling with ETA indices for cytotoxicity and enzymatic activity of diverse chemicals.

The discharge of huge amount of chemicals from industries into the environment has led to toxicity towards different living species. Therefore, risk assessment of these chemicals is essential. In order to comply with the ethical issues, in this present work, we have developed quantitative structure-activity relationship (QSAR) models for cytotoxicity against GFS (goldfish scale) tissue (Crassius auratus) and enzymatic activity against PLHC-1 cell line (topminnow hepatoma cell line) (Poeciliopsis lucida). The final models were developed by means of PLS (Partial Least Squares) regression method applying only ETA (extended topochemical atom) descriptors. The results obtained from various validation parameters (obtained from the both datasets) suggested that the developed models are statistically robust and predictive. From the insights obtained from the models developed from the Neutral Red dye (NR) dataset, it can be concluded that presence of bulky atoms, unsaturation, branching and hetero atoms (most importantly N, Cl) enhance the cytotoxicity towards the Goldfish scale tissue. On the other hand, in case of the Ethoxyresorufin-O-deethylase (EROD) dataset, presence of higher electronegative atoms (O, Cl), polycyclic aromatic hydrocarbons (PAHs) with more number of rings and absence of polar groups and hydrogen bond acceptors enhance enzymatic activity of the PLHC-1 cell line.

Cheminformatic modelling of ß-amyloid aggregation inhibitory activity against Alzheimer's disease.

In the current research, we have developed robust two-dimensional quantitative structure-activity relationship (2D-QSAR) and pharmacophore models using a dataset of 314 heterocyclic ß-amyloid aggregation inhibitors. The main purpose of this study is to determine the essential structural features which are responsible for the inhibition of ß-amyloid aggregation. Prior to the development of the 2D-QSAR model, we applied a multilayered variable selection method to reduce the size of the pool of descriptors, and the final models were built by the partial least squares (PLS) regression technique. The models obtained were thoroughly analysed by applying both internal and external validation parameters. The validation metrics obtained from the analysis suggested that the developed models were significant and sufficient to predict the inhibitory activity of unknown compounds. The structural features obtained from the pharmacophore model, such as the presence of aromatic rings and hydrogen bond acceptor/donor or hydrophobic sites, are well corroborated with those of the 2D-QSAR models. Additionally, we also performed a molecular docking study to understand the molecular interactions involved in binding, and the results were then correlated with the requisite structural features obtained from the 2D-QSAR and 3D-pharmacophore models.